SOTAVerified

AutoML

Automated Machine Learning (AutoML) is a general concept which covers diverse techniques for automated model learning including automatic data preprocessing, architecture search, and model selection. Source: Evaluating recommender systems for AI-driven data science (1905.09205)

Source: CHOPT : Automated Hyperparameter Optimization Framework for Cloud-Based Machine Learning Platforms

Papers

Showing 401450 of 641 papers

TitleStatusHype
Automating Data Science: Prospects and Challenges0
Chameleon: A Semi-AutoML framework targeting quick and scalable development and deployment of production-ready ML systems for SMEs0
Bag of Baselines for Multi-objective Joint Neural Architecture Search and Hyperparameter OptimizationCode1
Efficient Relation-aware Scoring Function Search for Knowledge Graph EmbeddingCode1
Search to aggregate neighborhood for graph neural networkCode1
AutoGL: A Library for Automated Graph LearningCode1
Model LineUpper: Supporting Interactive Model Comparison at Multiple Levels for AutoML0
Direct Differentiable Augmentation SearchCode1
How Powerful are Performance Predictors in Neural Architecture Search?0
EfficientNetV2: Smaller Models and Faster TrainingCode3
Auto-KWS 2021 Challenge: Task, Datasets, and BaselinesCode1
Bit-Mixer: Mixed-precision networks with runtime bit-width selection0
AlphaEvolve: A Learning Framework to Discover Novel Alphas in Quantitative InvestmentCode3
Rethinking Neural Operations for Diverse TasksCode1
Robust and Accurate Object Detection via Adversarial LearningCode3
Naive Automated Machine Learning -- A Late Baseline for AutoML0
Metalearning Using Structure-rich Pipeline Representations for Better AutoML0
Neural Architecture Search based on Cartesian Genetic Programming Coding Method0
An Automated Machine Learning (AutoML) Method for Driving Distraction Detection Based on Lane-Keeping Performance0
Improving Neural Networks for Time Series Forecasting using Data Augmentation and AutoMLCode0
Task-Adaptive Neural Network Search with Meta-Contrastive LearningCode1
Multi-Objective Evolutionary Design of Composite Data-Driven ModelsCode1
Automated Machine Learning on Graphs: A SurveyCode1
Automated Creative Optimization for E-Commerce AdvertisingCode0
Interpret-able feedback for AutoML systems0
Conditional Positional Encodings for Vision TransformersCode1
An AutoML-based Approach to Multimodal Image Sentiment Analysis0
CATE: Computation-aware Neural Architecture Encoding with TransformersCode1
Dancing along Battery: Enabling Transformer with Run-time Reconfigurability on Mobile Devices0
Leveraging Benchmarking Data for Informed One-Shot Dynamic Algorithm Selection0
Data Analytics and Machine Learning Methods, Techniques and Tool for Model-Driven Engineering of Smart IoT Services0
Incremental Search Space Construction for Machine Learning Pipeline Synthesis0
PyGlove: Symbolic Programming for Automated Machine Learning0
JITuNE: Just-In-Time Hyperparameter Tuning for Network Embedding Algorithms0
Robusta: Robust AutoML for Feature Selection via Reinforcement Learning0
A Neophyte With AutoML: Evaluating the Promises of Automatic Machine Learning ToolsCode0
AutoDS: Towards Human-Centered Automation of Data Science0
Whither AutoML? Understanding the Role of Automation in Machine Learning Workflows0
Fits and Starts: Enterprise Use of AutoML and the Role of Humans in the Loop0
How Much Automation Does a Data Scientist Want?0
Application of an automated machine learning-genetic algorithm (AutoML-GA) coupled with computational fluid dynamics simulations for rapid engine design optimization0
Hyperboost: Hyperparameter Optimization by Gradient Boosting surrogate models0
DiffAutoML: Differentiable Joint Optimization for Efficient End-to-End Automated Machine Learning0
Real-Time AutoML0
ECONOMIC HYPERPARAMETER OPTIMIZATION WITH BLENDED SEARCH STRATEGY0
Joint Search of Data Augmentation Policies and Network Architectures0
Squirrel: A Switching Hyperparameter OptimizerCode1
Amazon SageMaker Autopilot: a white box AutoML solution at scale0
Ensemble Squared: A Meta AutoML System0
Efficient Automatic CASH via Rising Bandits0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1marc.boulleRank (AutoML5)6.4Unverified
2reference_mbRank (AutoML5)5.2Unverified
3postech.mlg_exbrainRank (AutoML5)5.2Unverified
4abhishek4Rank (AutoML5)4.6Unverified
5referenceRank (AutoML5)4.4Unverified
6reference_lsRank (AutoML5)4Unverified
7djajeticRank (AutoML5)3Unverified
8aad_freiburgRank (AutoML5)1.6Unverified
#ModelMetricClaimedVerifiedStatus
1Logistic RegressionAccuracy97.02Unverified
#ModelMetricClaimedVerifiedStatus
1Zero-shot-BERT-SORT1:1 Accuracy55Unverified
#ModelMetricClaimedVerifiedStatus
1Logistic Regressionaccuracy98.33Unverified